Systems of the foregoing type are well known. For instance, in the credit industry, credit history information on a given business entity being considered for credit is typically processed through a commercially available database, such as a Dun & Bradstreet database. A user may input the name of a business entity into a processor connected to the database, which then locates that given entity in the database and retrieves its credit history information. The credit history information is then used to make a decision on whether to grant or withhold credit for the given entity.
To simplify matters with a simple example, assume that the user has an interest in making a sale on credit to XYZ Corp., which is located at a particular address in a particular city. XYZ Corp. is the "given entity," or "given entry." After the user inputs this identifying information, the database is searched and an entry for XYZ Corp. located at a different address in the same city is identified from the database. A determination must then be made as to whether the identified XYZ Corp. is the same as the given entity XYZ Corp. If the determination is that they are the same, then the credit information from the database for the identified XYZ Corp. is used in making the credit decision for the transaction with the given entity.
Database systems such as these have far reaching applications beyond credit industry applications as illustrated above. In another illustration, a wholesale distribution entity may periodically distribute product information documents to retail entities. The costs associated with these documents may range from inexpensive product brochures (e.g., 50 cents each) to relatively costly product catalogs (e.g., $5.00 each). In order to save costs, since thousands of these product information documents may be distributed, the wholesale distribution entity may wish to direct the more expensive catalogs to those retailers having a high sales volume, and the less expensive brochures to retailers having a low volume of sales. In this application, the database system would be accessed to identify sales information on certain entities, as opposed to credit history information.
As will become apparent from the discussion that follows, the present invention is useful in broad-ranging applications, including both of the foregoing illustrations. In order to better explain the concepts and teachings on the present invention, however, the illustrations provided hereinafter will generally focus on the credit industry application presented above.
Business entities are typically listed in a database by what can be called attributes. The most common attributes are those which identify the entity, such as the business name and location. Location can be broken down into a number of attributes which include street number, street name, P.O. box number, city, town or the like, state (if in the U.S.) or country, and telephone number. These are common attributes which are found in many commercial databases reporting information on business entities. Other attributes are, however, sometimes utilized.
When it is desired to find a match for a given entity within such a list of business entities, inconsistencies in listing information can create matching problems. In some instances, inconsistencies can result from erroneous information stored in the database itself, and also from erroneous information input when identifying a given entity for whom a match is desired. In other instances, inconsistencies may result merely due to differing styles (e.g., abbreviations) used to identify certain attributes.
Credit departments typically have procedures for dialing up databases and obtaining credit information. Usually, the identification process is rather straightforward, and may be performed automatically. However, because of the different styles of stating names and addresses and the different care which is exercised by a large number of people in collecting information, the correlation between a given entity and the possible matching entities in the database do not always match precisely. When this occurs, human intervention is often necessary to make the intermediate determination as to which one of the one or more identified entities matches the given entity, before the ultimate determination of whether to grant or withhold credit can be made. Proper intermediate identification is particularly important in large dollar transactions. The human intervention usually involves either making an on-the-spot judgment as to the correct match, or making follow-up phone calls to investigate or verify the given entity.
Based on the amount of time required to verify the identity of a given entity, and the cost associated with the human (e.g., credit manager, clerk, etc.) who makes those decisions, it will be found that this somewhat mundane step in the credit approval procedure can consume a significant amount of dollar resources. Indeed, in situations where a large number of such credit decisions are made, it is found to be commercially feasible to isolate a subset of justifiable risks (i.e., those where a reliable match is made), and grant credit to those risks without the need for human intervention.
There are generally available processes and procedures, and commercially available software packages for determining a "best fit" match for any given entity within a large compilation or list of entities. For example, a system known as Soundex is well known and has long been used to find words that sound similar but are spelled differently. Similarly, a system known as AdMatch was used to help people find the proper 1970 census tract, using a base address.
In the credit industry, systems like the foregoing are used by credit reporting agencies to identify a list of possible matching entities and numerically score the match of the identifying attributes (name, address, city, etc.) for each entity identified. More particularly, automated matching systems are available, which parse, normalize, and further process a given entry to identify likely matches. These systems can also provide attribute-by-attribute information, such as a numerical score, reflecting the reliability of the match of each attribute. Thus, a user might be faced with an attempted match where the name matches exactly and thus has a 100% score, the street address has a 63% score, the town 79%, and the phone number a no entry condition. But, again, human intervention is usually required as a credit manager, clerk, or other appropriate person must examine the entries, the scores, and the overall context of the request in order to determine whether the information provided by the credit database indeed matches the characteristics of the given entity.
More sophisticated systems are known, wherein the individual attribute scores are weighted by factors based on empirical data to produce a composite score. These systems have been less than effective in the past, and it is typically found that programmers are continuously adjusting weighting factors to accommodate new conditions. As additional empirical data is collected, the weighting algorithm be further refined. Thus, it can be appreciated that the weighting function or algorithm is a ever-changing device. Unfortunately, while the newly adjusted weighting factors may accommodate a new condition successfully, they often unexpectedly and adversely affect other computations, and accurate matching problems persist.
It will be seen that a significant cost savings can be achieved by further automating and improving the credit approval process, thereby reducing or eliminating the need for a human to become personally involved.